An Unsupervised Approach for Aspect Category Detection Using Soft Cosine Similarity Measure

Ghadery, Erfan; Movahedi, Sajad; Faili, Heshaam; Shakery, Azadeh

doi:10.48550/arxiv.1812.03361

Cited by 3 publications

(3 citation statements)

References 14 publications

(18 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While there are approaches that rely mainly on clustering techniques, they are less frequent. An example of a clustering-based approach is that of Ghadery et al [45], who use k-means clustering on representations of sentences obtained by averaging word2vec embeddings and a soft cosine similarity measure, to determine the similarity between a sentence and an aspect category, represented by a set of seed words.…”

Section: Aspect Term Extraction (Ate) and Aspect Category Detection (...mentioning

confidence: 99%

Machine-Learning-Based Approaches for Multi-Level Sentiment Analysis of Romanian Reviews

Briciu,

Călin,

Miholca

et al. 2024

Mathematics

View full text Add to dashboard Cite

Sentiment analysis has increasingly gained significance in commercial settings, driven by the rising impact of reviews on purchase decision-making in recent years. This research conducts a thorough examination of the suitability of machine learning and deep learning approaches for sentiment analysis, using Romanian reviews as a case study, with the aim of gaining insights into their practical utility. A comprehensive, multi-level analysis is performed, covering the document, sentence, and aspect levels. The main contributions of the paper refer to the in-depth exploration of multiple sentiment analysis models at three different textual levels and the subsequent improvements brought with respect to these standard models. Additionally, a balanced dataset of Romanian reviews from twelve product categories is introduced. The results indicate that, at the document level, supervised deep learning techniques yield the best outcomes (specifically, a convolutional neural network model that obtains an AUC value of 0.93 for binary classification and a weighted average F1-score of 0.77 in a multi-class setting with 5 target classes), albeit with increased resource consumption. Favorable results are achieved at the sentence level, as well, despite the heightened complexity of sentiment identification. In this case, the best-performing model is logistic regression, for which a weighted average F1-score of 0.77 is obtained in a multi-class polarity classification task with three classes. Finally, at the aspect level, promising outcomes are observed in both aspect term extraction and aspect category detection tasks, in the form of coherent and easily interpretable word clusters, encouraging further exploration in the context of aspect-based sentiment analysis for the Romanian language.

show abstract

Section: Aspect Term Extraction (Ate) and Aspect Category Detection (...mentioning

confidence: 99%

Machine-Learning-Based Approaches for Multi-Level Sentiment Analysis of Romanian Reviews

Briciu,

Călin,

Miholca

et al. 2024

Mathematics

View full text Add to dashboard Cite

show abstract

“…For the unsupervised method, the first method of this approach that applied association rule mining on co-occurrence frequency data obtained from a corpus to find these aspect categories is presented by Schouten et al [2] and Ghadery et al [4]. However, the functional drawback of this unsupervised approach is that it involves the tuning of several parameters.…”

Section: Related Workmentioning

confidence: 99%

Investigating Monolingual and Multilingual BERTModels for Vietnamese Aspect Category Detection

Thin¹,

Le²,

Hoang³

et al. 2021

Preprint

View full text Add to dashboard Cite

Aspect category detection (ACD) is one of the challenging tasks in the Aspect-based sentiment Analysis problem. The purpose of this task is to identify the aspect categories mentioned in user-generated reviews from a set of pre-defined categories. In this paper, we investigate the performance of various monolingual pre-trained language models compared with multilingual models on the Vietnamese aspect category detection problem. We conduct the experiments on two benchmark datasets for the restaurant and hotel domain. The experimental results demonstrated the effectiveness of the monolingual PhoBERT model than others on two datasets. We also evaluate the performance of the multilingual model based on the combination of whole SemEval-2016 datasets in other languages with the Vietnamese dataset. To the best of our knowledge, our research study is the first attempt at performing various available pretrained language models on aspect category detection task and utilize the datasets from other languages based on multilingual models.

show abstract

“…Our chain classifier uses combined image and text features as the input. We transfer the predicted probabilities of the classifier via the sigmoid activation function to make the probability values more discriminating (Ghadery et al, 2018). Then we apply thresholding on the L labels probabilities since the task requires a discrete set of labels as output.…”

Section: Chained Classifiermentioning

confidence: 99%

LIIR at SemEval-2021 task 6: Detection of Persuasion Techniques In Texts and Images using CLIP features

Ghadery¹,

Sileo²,

Moens³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

An Unsupervised Approach for Aspect Category Detection Using Soft Cosine Similarity Measure

Cited by 3 publications

References 14 publications

Machine-Learning-Based Approaches for Multi-Level Sentiment Analysis of Romanian Reviews

Machine-Learning-Based Approaches for Multi-Level Sentiment Analysis of Romanian Reviews

Investigating Monolingual and Multilingual BERTModels for Vietnamese Aspect Category Detection

LIIR at SemEval-2021 task 6: Detection of Persuasion Techniques In Texts and Images using CLIP features

Contact Info

Product

Resources

About